ChEMBL is an Open Data database containing binding, functional and ADMET information for a large number of drug-like bioactive compounds. These data are manually abstracted from the primary published literature on a regular basis, then further curated and standardized to maximize their quality and utility across a wide range of chemical biology and drug-discovery research problems. Currently, the database contains 5.4 million bioactivity measurements for more than 1 million compounds and 5200 protein targets. Access is available through a web-based interface, data downloads and web services at: https://www.ebi.ac.uk/chembldb.

/pdf/chembl-a-large-scale-bioactivity-database-for-drug-discovery-4mahqu7a7p.pdf

ChEMBL: a large-scale bioactivity database for drug discovery

http://staff.unila.ac.id/ekoefendi/files/2011/10/Malczewski_2004.pdf

GIS-based land-use suitability analysis: a critical overview

Recommender systems improve access to relevant products and information by making personalized suggestions based on previous examples of a user's likes and dislikes. Most existing recommender systems use collaborative filtering methods that base recommendations on other users' preferences. By contrast,content-based methods use information about an item itself to make suggestions.This approach has the advantage of being able to recommend previously unrated items to users with unique interests and to provide explanations for its recommendations. We describe a content-based book recommending system that utilizes information extraction and a machine-learning algorithm for text categorization. Initial experimental results demonstrate that this approach can produce accurate recommendations.

/pdf/content-based-book-recommending-using-learning-for-text-4mustpccpe.pdf

Content-based book recommending using learning for text categorization

Recommender systems improve access to relevant products and information by making personalized suggestions based on previous examples of a user's likes and dislikes. Most existing recommender systems use social filtering methods that base recommendations on other users' preferences. By contrast, content-based methods use information about an item itself to make suggestions. This approach has the advantage of being able to recommended previously unrated items to users with unique interests and to provide explanations for its recommendations. We describe a content-based book recommending system that utilizes information extraction and a machine-learning algorithm for text categorization. Initial experimental results demonstrate that this approach can produce accurate recommendations.

Content-Based Book Recommending Using Learning for Text Categorization

Information science emerged as the third subject, along with logic and philosophy, to deal with relevance-an elusive, human notion. The concern with relevance, as a key notion in information science, is traced to the problems of scientific communication. Relevance is considered as a measure of the effectiveness of a contact between a source and a destination in a communication process. The different views of relevance that emerged are interpreted and related within a framework of communication of knowledge. Different views arose because relevance was considered at a number of different points in the process of knowledge communication. It is suggested that there exists an interlocking, interplaying cycle of various systems of relevances.

Allen Kent

Papers

Belgium, libraries in

The Russians have a machine for it, a review

Machine literature searching III. Making indexes amenable to machine searching

Encyclopedia of library and information science. Supplement

Machine literature searching IX. Operational functions of automatic equipment